Modeling music as Markov chains - composer identification

Music 254 Final Report
Yi-Wen Liu (jacobliu@ccrma.stanford.edu)

Professor Eleanor Selfridge-Field
TA: Craig Sapp

Date: 10 June 2002

Abstract:

In this research, it is proposed that music can be thought of as
random processes, and music style recognition can be thought of as
a system identification problem. Then, a general framework for modeling
music using Markov chains is described, and based on this framework,
a two-way composer identification scheme is demonstrated.
The scheme utilizes the Kullback-Leibler distance as the metric
between distribution functions, and it is shown that under the condition
when the marginals are identical, the scheme gives maximum likelihood
identification. Experiments of composer identification are conducted on
all the string quartets written by Mozart and Haydn, and the results
are documented and discussed.